In a graph database, entities are presented as nodes and relationships between those entities are presented as edges in a view of the relational graph maintained by the graph database. The relational graph may be periodically queried by users via graph queries to learn about the relationships between entities. For example, a user may issue a graph query to find friends or contacts within a social network, the documents that a given user has interacted with, the users that a given document has been accessed by, the entities that satisfy various search criteria, etc. The graph query produces a subgraph of the queried relational graph that satisfies the query, which may be further analyzed by the user. Graph queries are computationally expensive to run, and may be limited in their functionality; requiring a user to run a graph query, extract the subgraph, and run separate analytics on the subgraph, often in a separate program or interface than that used to perform the graph query, which adds additional computation expense for data analysis or relational graphs.